code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      weight_attr=None,
      bias_attr=None,
      lr_scale=1,
      regularizer=None,
      skip_quant=False,
      dcn_bias_regularizer=L2Decay(0.0),
      dcn_bias_lr_scale=2.0,
  ):
      super(DeformableConvV2, self).__init__()
      self.offset_channel = 2 * kernel_size**2 * groups
      self.mask_channel = kernel_size**2 * groups
  
      if bias_attr:
          # in FCOS-DCN head, specifically need learning_rate and regularizer
          dcn_bias_attr = ParamAttr(
              initializer=Constant(value=0),
              regularizer=dcn_bias_regularizer,
              learning_rate=dcn_bias_lr_scale,
          )
      else:
          # in ResNet backbone, do not need bias
          dcn_bias_attr = False
      self.conv_dcn = DeformConv2D(
          in_channels,
          out_channels,
          kernel_size,
          stride=stride,
          padding=(kernel_size - 1) // 2 * dilation,
          dilation=dilation,
          deformable_groups=groups,
          weight_attr=weight_attr,
          bias_attr=dcn_bias_attr,
      )
  
      if lr_scale == 1 and regularizer is None:
          offset_bias_attr = ParamAttr(initializer=Constant(0.0))
      else:
          offset_bias_attr = ParamAttr(
              initializer=Constant(0.0),
              learning_rate=lr_scale,
              regularizer=regularizer,
          )
      self.conv_offset = nn.Conv2D(
          in_channels,
          groups * 3 * kernel_size**2,
          kernel_size,
          stride=stride,
          padding=(kernel_size - 1) // 2,
          weight_attr=ParamAttr(initializer=Constant(0.0)),
          bias_attr=offset_bias_attr,
      )
      if skip_quant:
          self.conv_offset.skip_quant = True
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.vision.ops import DeformConv2D
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant, XavierUniform
label_desc: Use image node_id 1 to create a new DeformableConvV2 object from inherited base classes: nn with example: obj = DeformableConvV2(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, weight_attr, bias_attr, lr_scale, regularizer, skip_quant, dcn_bias_regularizer, dcn_bias_lr_scale)